UCL Discovery
UCL home » Library Services » Electronic resources » UCL Discovery

Comparison of CST with different hours of storage in the Australian National Electricity Market

Wu, Y; Reedman, LJ; Barrett, M; Spataru, C; (2018) Comparison of CST with different hours of storage in the Australian National Electricity Market. Renewable Energy , 122 pp. 487-496. 10.1016/j.renene.2018.02.014. Green open access

[img]
Preview
Text
Spataru_Manuscript_Wu_et_al_20171014.pdf - Accepted version

Download (1MB) | Preview

Abstract

The recent ratification of the Paris Climate Change Agreement has significant implications for Australia given its emissions intensive economy. It is likely that the electricity sector will need to decarbonize for Australia to meet medium- and long-term emissions reduction targets. This paper explored the potential role of Concentrating Solar Thermal (CST) in a 100 per cent renewable National Electricity Market (NEM) system under different scenarios of CST configuration and subjected the results to sensitivity analysis. A Genetic algorithm (GA) was chosen as the optimization algorithm to seek the least cost combination of renewable generation technologies, transmission interconnectors and storage capacity in the NEM system at hourly temporal resolution. The main finding is that the scenario where all three CST configurations (six, nine, and twelve hours of thermal storage) can be deployed achieves a lower system cost than scenarios where the size of thermal storage coupled with CST is limited to one option. The results are sensitive to assumptions of the discount rate, renewable resource availability, and the cost of CST technology. This paper found that meeting demand during winter evenings is the most challenging time period for a 100 per cent renewable NEM power system.

Type: Article
Title: Comparison of CST with different hours of storage in the Australian National Electricity Market
Open access status: An open access version is available from UCL Discovery
DOI: 10.1016/j.renene.2018.02.014
Publisher version: https://doi.org/10.1016/j.renene.2018.02.014
Language: English
Additional information: © 2018 Published by Elsevier Ltd. This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Energy modelling;, optimization;, 100 per cent renewables;, least cost scenarios
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment > Bartlett School Env, Energy and Resources
URI: https://discovery.ucl.ac.uk/id/eprint/10042812
Downloads since deposit
42Downloads
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item